code: def launch_command_parser(subparsers=None):
      if subparsers is not None:
          parser = subparsers.add_parser(
              "launch", add_help=False, allow_abbrev=False
          )
      else:
          parser = argparse.ArgumentParser(
              "Accelerate launch command",
              add_help=False,
              allow_abbrev=False,
          )
  
      parser.register("action", "help", _CustomHelpAction)
      parser.add_argument(
          "-h",
          "--help",
          action="help",
          help="Show this help message and exit.",
      )
  
      parser.add_argument(
          "--config_file",
          default=None,
          help="The config file to use for the default values in the launching script.",
      )
      parser.add_argument(
          "--quiet",
          "-q",
          action="store_true",
          help="Silence subprocess errors from the launch stack trace and only show the relevant tracebacks. (Only applicable to DeepSpeed and single-process configurations)",
      )
      # Hardware selection arguments
      hardware_args = parser.add_argument_group(
          "Hardware Selection Arguments",
          "Arguments for selecting the hardware to be used.",
      )
      hardware_args.add_argument(
          "--cpu",
          default=False,
          action="store_true",
          help="Whether or not to force the training on the CPU.",
      )
      hardware_args.add_argument(
          "--multi_gpu",
          default=False,
          action="store_true",
          help="Whether or not this should launch a distributed GPU training.",
      )
      hardware_args.add_argument(
          "--tpu",
          default=False,
          action="store_true",
          help="Whether or not this should launch a TPU training.",
      )
      hardware_args.add_argument(
          "--ipex",
          default=False,
          action="store_true",
          help="Whether or not this should launch a Intel PyTorch Extension (IPEX) training.",
      )
  
      # Resource selection arguments
      resource_args = parser.add_argument_group(
          "Resource Selection Arguments",
          "Arguments for fine-tuning how available hardware should be used.",
      )
      resource_args.add_argument(
          "--mixed_precision",
          type=str,
          choices=["no", "fp16", "bf16", "fp8"],
          help="Whether or not to use mixed precision training. "
          "Choose between FP16 and BF16 (bfloat16) training. "
          "BF16 training is only supported on Nvidia Ampere GPUs and PyTorch 1.10 or later.",
      )
      resource_args.add_argument(
          "--num_processes",
          type=int,
          default=None,
          help="The total number of processes to be launched in parallel.",
      )
      resource_args.add_argument(
          "--num_machines",
          type=int,
          default=None,
          help="The total number of machines used in this training.",
      )
      resource_args.add_argument(
          "--num_cpu_threads_per_process",
          type=int,
          default=None,
          help="The number of CPU threads per process. Can be tuned for optimal performance.",
      )
  
      # Dynamo arguments
      resource_args.add_argument(
          "--dynamo_backend",
          type=str,
          choices=["no"] + [b.lower() for b in DYNAMO_BACKENDS],
          help="Choose a backend to optimize your training with dynamo, see more at "
          "https://github.com/pytorch/torchdynamo.",
      )
      resource_args.add_argument(
          "--dynamo_mode",
          type=str,
          default="default",
          choices=TORCH_DYNAMO_MODES,
          help="Choose a mode to optimize your training with dynamo.",
      )
      resource_args.add_argument(
          "--dynamo_use_fullgraph",
          default=False,
          action="store_true",
          help="Whether to use full graph mode for dynamo or it is ok to break model into several subgraphs",
      )
      resource_args.add_argument(
          "--dynamo_use_dynamic",
          default=False,
          action="store_true",
          help="Whether to enable dynamic shape tracing.",
      )
  
      # Training Paradigm arguments
      paradigm_args = parser.add_argument_group(
          "Training Paradigm Arguments",
          "Arguments for selecting which training paradigm to be used.",
      )
      paradigm_args.add_argument(
          "--use_deepspeed",
          default=False,
          action="store_true",
          help="Whether to use deepspeed.",
      )
      paradigm_args.add_argument(
          "--use_fsdp",
          default=False,
          action="store_true",
          help="Whether to use fsdp.",
      )
      paradigm_args.add_argument(
          "--use_megatron_lm",
          default=False,
          action="store_true",
          help="Whether to use Megatron-LM.",
      )
      paradigm_args.add_argument(
          "--use_xpu",
          default=False,
          action="store_true",
          help="Whether to use IPEX plugin to speed up training on XPU specifically.",
      )
  
      # distributed GPU training arguments
      distributed_args = parser.add_argument_group(
          "Distributed GPUs",
          "Arguments related to distributed GPU training.",
      )
      distributed_args.add_argument(
          "--gpu_ids",
          default=None,
          help="What GPUs (by id) should be used for training on this machine as a comma-seperated list",
      )
      distributed_args.add_argument(
          "--same_network",
          default=False,
          action="store_true",
          help="Whether all machines used for multinode training exist on the same local network.",
      )
      distributed_args.add_argument(
          "--machine_rank",
          type=int,
          default=None,
          help="The rank of the machine on which this script is launched.",
      )
      distributed_args.add_argument(
          "--main_process_ip",
          type=str,
          default=None,
          help="The IP address of the machine of rank 0.",
      )
      distributed_args.add_argument(
          "--main_process_port",
          type=int,
          default=None,
          help="The port to use to communicate with the machine of rank 0.",
      )
      distributed_args.add_argument(
          "-t",
          "--tee",
          default="0",
          type=str,
          help="Tee std streams into a log file and also to console.",
      )
      distributed_args.add_argument(
          "--role",
          type=str,
          default="default",
          help="User-defined role for the workers.",
      )
      # Rendezvous related arguments
      distributed_args.add_argument(
          "--rdzv_backend",
          type=str,
          default="static",
          help="The rendezvous method to use, such as 'static' (the default) or 'c10d'",
      )
      distributed_args.add_argument(
          "--rdzv_conf",
          type=str,
          default="",
          help="Additional rendezvous configuration (<key1>=<value1>,<key2>=<value2>,...).",
      )
      distributed_args.add_argument(
          "--max_restarts",
          type=int,
          default=0,
          help="Maximum number of worker group restarts before failing.",
      )
      distributed_args.add_argument(
          "--monitor_interval",
          type=float,
          default=5,
          help="Interval, in seconds, to monitor the state of workers.",
      )
      parser.add_argument(
          "-m",
          "--module",
          action="store_true",
          help="Change each process to interpret the launch script as a Python module, executing with the same behavior as 'python -m'.",
      )
      parser.add_argument(
          "--no_python",
          action="store_true",
          help="Skip prepending the training script with 'python' - just execute it directly. Useful when the script is not a Python script.",
      )
  
      # TPU arguments
      tpu_args = parser.add_argument_group(
          "TPU", "Arguments related to TPU."
      )
      tpu_args.add_argument(
          "--tpu_cluster",
          action="store_true",
          dest="tpu_use_cluster",
          help="Whether to use a GCP TPU pod for training.",
      )
      tpu_args.add_argument(
          "--no_tpu_cluster",
          action="store_false",
          dest="tpu_use_cluster",
          help="Should not be passed explicitly, this is for internal use only.",
      )
      tpu_args.add_argument(
          "--tpu_use_sudo",
          action="store_true",
          help="Whether to use `sudo` when running the TPU training script in each pod.",
      )
      tpu_args.add_argument(
          "--vm",
          type=str,
          action="append",
          help=(
              "List of single Compute VM instance names. "
              "If not provided we assume usage of instance groups. For TPU pods."
          ),
      )
      tpu_args.add_argument(
          "--env",
          type=str,
          action="append",
          help="List of environment variables to set on the Compute VM instances. For TPU pods.",
      )
      tpu_args.add_argument(
          "--main_training_function",
          type=str,
          default=None,
          help="The name of the main function to be executed in your script (only for TPU training).",
      )
      tpu_args.add_argument(
          "--downcast_bf16",
          action="store_true",
          help="Whether when using bf16 precision on TPUs if both float and double tensors are cast to bfloat16 or if double tensors remain as float32.",
      )
  
      # DeepSpeed arguments
      deepspeed_args = parser.add_argument_group(
          "DeepSpeed Arguments", "Arguments related to DeepSpeed."
      )
      deepspeed_args.add_argument(
          "--deepspeed_config_file",
          default=None,
          type=str,
          help="DeepSpeed config file.",
      )
      deepspeed_args.add_argument(
          "--zero_stage",
          default=None,
          type=int,
          help="DeepSpeed's ZeRO optimization stage (useful only when `use_deepspeed` flag is passed). "
          "If unspecified, will default to `2`.",
      )
      deepspeed_args.add_argument(
          "--offload_optimizer_device",
          default=None,
          type=str,
          help="Decides where (none|cpu|nvme) to offload optimizer states (useful only when `use_deepspeed` flag is passed). "
          "If unspecified, will default to 'none'.",
      )
      deepspeed_args.add_argument(
          "--offload_param_device",
          default=None,
          type=str,
          help="Decides where (none|cpu|nvme) to offload parameters (useful only when `use_deepspeed` flag is passed). "
          "If unspecified, will default to 'none'.",
      )
      deepspeed_args.add_argument(
          "--offload_optimizer_nvme_path",
          default=None,
          type=str,
          help="Decides Nvme Path to offload optimizer states (useful only when `use_deepspeed` flag is passed). "
          "If unspecified, will default to 'none'.",
      )
      deepspeed_args.add_argument(
          "--offload_param_nvme_path",
          default=None,
          type=str,
          help="Decides Nvme Path to offload parameters (useful only when `use_deepspeed` flag is passed). "
          "If unspecified, will default to 'none'.",
      )
      deepspeed_args.add_argument(
          "--gradient_accumulation_steps",
          default=None,
          type=int,
          help="No of gradient_accumulation_steps used in your training script (useful only when `use_deepspeed` flag is passed). "
          "If unspecified, will default to `1`.",
      )
      deepspeed_args.add_argument(
          "--gradient_clipping",
          default=None,
          type=float,
          help="gradient clipping value used in your training script (useful only when `use_deepspeed` flag is passed). "
          "If unspecified, will default to `1.0`.",
      )
      deepspeed_args.add_argument(
          "--zero3_init_flag",
          default=None,
          type=str,
          help="Decides Whether (true|false) to enable `deepspeed.zero.Init` for constructing massive models. "
          "Only applicable with DeepSpeed ZeRO Stage-3. If unspecified, will default to `true`.",
      )
      deepspeed_args.add_argument(
          "--zero3_save_16bit_model",
          default=None,
          type=str,
          help="Decides Whether (true|false) to save 16-bit model weights when using ZeRO Stage-3. "
          "Only applicable with DeepSpeed ZeRO Stage-3. If unspecified, will default to `false`.",
      )
      deepspeed_args.add_argument(
          "--deepspeed_hostfile",
          default=None,
          type=str,
          help="DeepSpeed hostfile for configuring multi-node compute resources.",
      )
      deepspeed_args.add_argument(
          "--deepspeed_exclusion_filter",
          default=None,
          type=str,
          help="DeepSpeed exclusion filter string when using mutli-node setup.",
      )
      deepspeed_args.add_argument(
          "--deepspeed_inclusion_filter",
          default=None,
          type=str,
          help="DeepSpeed inclusion filter string when using mutli-node setup.",
      )
      deepspeed_args.add_argument(
          "--deepspeed_multinode_launcher",
          default=None,
          type=str,
          help="DeepSpeed multi-node launcher to use. If unspecified, will default to `pdsh`.",
      )
  
      # fsdp arguments
      fsdp_args = parser.add_argument_group(
          "FSDP Arguments",
          "Arguments related to Fully Shared Data Parallelism.",
      )
      fsdp_args.add_argument(
          "--fsdp_offload_params",
          default="false",
          type=str,
          help="Decides Whether (true|false) to offload parameters and gradients to CPU. (useful only when `use_fsdp` flag is passed).",
      )
      fsdp_args.add_argument(
          "--fsdp_min_num_params",
          type=int,
          default=1e8,
          help="FSDP's minimum number of parameters for Default Auto Wrapping. (useful only when `use_fsdp` flag is passed).",
      )
      fsdp_args.add_argument(
          "--fsdp_sharding_strategy",
          type=str,
          default="FULL_SHARD",
          help="FSDP's Sharding Strategy. (useful only when `use_fsdp` flag is passed).",
      )
      fsdp_args.add_argument(
          "--fsdp_auto_wrap_policy",
          type=str,
          default=None,
          help="FSDP's auto wrap policy. (useful only when `use_fsdp` flag is passed).",
      )
      fsdp_args.add_argument(
          "--fsdp_transformer_layer_cls_to_wrap",
          default=None,
          type=str,
          help="Transformer layer class name (case-sensitive) to wrap ,e.g, `BertLayer`, `GPTJBlock`, `T5Block` .... "
          "(useful only when `use_fsdp` flag is passed).",
      )
      fsdp_args.add_argument(
          "--fsdp_backward_prefetch_policy",
          default=None,
          type=str,
          help="This argument is deprecated and will be removed in version 0.27.0 of 🤗 Accelerate. Use `fsdp_backward_prefetch` instead.",
      )
      fsdp_args.add_argument(
          "--fsdp_backward_prefetch",
          default=None,
          type=str,
          help="FSDP's backward prefetch policy. (useful only when `use_fsdp` flag is passed).",
      )
      fsdp_args.add_argument(
          "--fsdp_state_dict_type",
          default=None,
          type=str,
          help="FSDP's state dict type. (useful only when `use_fsdp` flag is passed).",
      )
      fsdp_args.add_argument(
          "--fsdp_forward_prefetch",
          default="false",
          type=str,
          help="If True, then FSDP explicitly prefetches the next upcoming "
          "all-gather while executing in the forward pass (useful only when `use_fsdp` flag is passed).",
      )
      fsdp_args.add_argument(
          "--fsdp_use_orig_params",
          default="true",
          type=str,
          help="If True, allows non-uniform `requires_grad` during init, which means support for interspersed frozen and trainable paramteres."
          " (useful only when `use_fsdp` flag is passed).",
      )
      fsdp_args.add_argument(
          "--fsdp_cpu_ram_efficient_loading",
          default="true",
          type=str,
          help="If True, only the first process loads the pretrained model checkoint while all other processes have empty weights. "
          "Only applicable for 🤗 Transformers. When using this, `--fsdp_sync_module_states` needs to True. "
          "(useful only when `use_fsdp` flag is passed).",
      )
      fsdp_args.add_argument(
          "--fsdp_sync_module_states",
          default="true",
          type=str,
          help="If True, each individually wrapped FSDP unit will broadcast module parameters from rank 0."
          " (useful only when `use_fsdp` flag is passed).",
      )
  
      # megatron_lm args
      megatron_lm_args = parser.add_argument_group(
          "Megatron-LM Arguments", "Arguments related to Megatron-LM."
      )
      megatron_lm_args.add_argument(
          "--megatron_lm_tp_degree",
          type=int,
          default=1,
          help="Megatron-LM's Tensor Parallelism (TP) degree. (useful only when `use_megatron_lm` flag is passed).",
      )
      megatron_lm_args.add_argument(
          "--megatron_lm_pp_degree",
          type=int,
          default=1,
          help="Megatron-LM's Pipeline Parallelism (PP) degree. (useful only when `use_megatron_lm` flag is passed).",
      )
      megatron_lm_args.add_argument(
          "--megatron_lm_num_micro_batches",
          type=int,
          default=None,
          help="Megatron-LM's number of micro batches when PP degree > 1. (useful only when `use_megatron_lm` flag is passed).",
      )
      megatron_lm_args.add_argument(
          "--megatron_lm_sequence_parallelism",
          default=None,
          type=str,
          help="Decides Whether (true|false) to enable Sequence Parallelism when TP degree > 1. "
          "(useful only when `use_megatron_lm` flag is passed).",
      )
      megatron_lm_args.add_argument(
          "--megatron_lm_recompute_activations",
          default=None,
          type=str,
          help="Decides Whether (true|false) to enable Selective Activation Recomputation. "
          "(useful only when `use_megatron_lm` flag is passed).",
      )
      megatron_lm_args.add_argument(
          "--megatron_lm_use_distributed_optimizer",
          default=None,
          type=str,
          help="Decides Whether (true|false) to use distributed optimizer "
          "which shards optimizer state and gradients across Data Pralellel (DP) ranks. "
          "(useful only when `use_megatron_lm` flag is passed).",
      )
      megatron_lm_args.add_argument(
          "--megatron_lm_gradient_clipping",
          default=1.0,
          type=float,
          help="Megatron-LM's gradient clipping value based on global L2 Norm (0 to disable). "
          "(useful only when `use_megatron_lm` flag is passed).",
      )
  
      # AWS arguments
      aws_args = parser.add_argument_group(
          "AWS Arguments", "Arguments related to AWS."
      )
      aws_args.add_argument(
          "--aws_access_key_id",
          type=str,
          default=None,
          help="The AWS_ACCESS_KEY_ID used to launch the Amazon SageMaker training job",
      )
      aws_args.add_argument(
          "--aws_secret_access_key",
          type=str,
          default=None,
          help="The AWS_SECRET_ACCESS_KEY used to launch the Amazon SageMaker training job.",
      )
      parser.add_argument(
          "--debug",
          action="store_true",
          help="Whether to print out the torch.distributed stack trace when something fails.",
      )
      parser.add_argument(
          "training_script",
          type=str,
          help=(
              "The full path to the script to be launched in parallel, followed by all the arguments for the training "
              "script."
          ),
      )
  
      # Other arguments of the training scripts
      parser.add_argument(
          "training_script_args",
          nargs=argparse.REMAINDER,
          help="Arguments of the training script.",
      )
  
      if subparsers is not None:
          parser.set_defaults(func=launch_command)
      return parser
  
imports: import argparse
  import importlib
  import logging
  import os
  import subprocess
  import sys
  from pathlib import Path
  import psutil
  import torch
  from accelerate.commands.config import default_config_file, load_config_from_file
  from accelerate.commands.config.config_args import SageMakerConfig
  from accelerate.commands.config.config_utils import DYNAMO_BACKENDS
  from accelerate.state import get_int_from_env
  from accelerate.utils import ComputeEnvironment, DistributedType, PrepareForLaunch, _filter_args, check_cuda_p2p_ib_support, is_bf16_available, is_deepspeed_available, is_npu_available, is_rich_available, is_sagemaker_available, is_torch_version, is_tpu_available, is_xpu_available, patch_environment, prepare_deepspeed_cmd_env, prepare_multi_gpu_env, prepare_sagemager_args_inputs, prepare_simple_launcher_cmd_env, prepare_tpu
  from accelerate.utils.constants import DEEPSPEED_MULTINODE_LAUNCHERS, TORCH_DYNAMO_MODES
label_desc: Use image node_id 2 for calling a global function with example usage: launch_command_parser(subparsers) and returns: parser